SparkSession and SparkContext in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is SparkContext in Apache Spark?

SparkContext is the entry point to Spark functionality. It connects your program to the Spark cluster and allows you to create RDDs (Resilient Distributed Datasets). Think of it as the main controller for Spark jobs.

beginner

What is SparkSession and why is it important?

SparkSession is the new entry point introduced in Spark 2.0. It combines SQLContext, HiveContext, and SparkContext into one object. It lets you work with DataFrames, SQL, and streaming easily in one place.

intermediate

How do SparkSession and SparkContext relate to each other?

SparkSession internally manages a SparkContext. When you create a SparkSession, it creates or uses an existing SparkContext. You usually use SparkSession now because it handles more features and is simpler.

beginner

How to create a SparkSession in PySpark?

You create a SparkSession using the builder pattern like this:<br><pre>from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('MyApp').getOrCreate()</pre><br>This starts Spark and lets you use DataFrames and SQL.

intermediate

Why should you prefer SparkSession over SparkContext in modern Spark applications?

SparkSession provides a unified interface for all Spark features like SQL, streaming, and machine learning. It simplifies code and manages SparkContext internally, so you don't have to handle multiple contexts separately.

What is the main role of SparkContext in Apache Spark?

A Connect your program to the Spark cluster and create RDDs

B Manage SQL queries only

C Visualize data in Spark

D Store data permanently

SparkContext connects your program to the Spark cluster and lets you create RDDs for distributed computing.

Which object should you use to work with DataFrames and SQL in Spark 2.0 and later?

A SparkContext

B SQLContext

C SparkSession

D HiveContext

SparkSession is the unified entry point for DataFrames, SQL, and other Spark features since Spark 2.0.

How do you create a SparkSession in PySpark?

A spark = SparkContext()

B spark = SparkSession.builder.appName('App').getOrCreate()

C spark = SQLContext()

D spark = HiveContext()

You create a SparkSession using the builder pattern with appName and getOrCreate() methods.

What does SparkSession internally manage?

A Only SQLContext

B No other context

C Only HiveContext

D SparkContext

SparkSession internally manages SparkContext to handle cluster connection and job execution.

Why is SparkSession preferred over SparkContext in modern Spark applications?

A It provides a unified interface for multiple Spark features

B It requires less memory

C It is slower but more reliable

D It only supports RDDs

SparkSession unifies access to SQL, streaming, machine learning, and RDDs, simplifying Spark programming.

Explain the difference between SparkContext and SparkSession and when to use each.

Think about how Spark evolved from SparkContext to SparkSession.

Describe how to create a SparkSession in PySpark and why it is important.

Focus on the code and the benefits of SparkSession.

Practice

1. What is the primary role of SparkSession in Apache Spark?

easy

A. It stores data permanently in Spark.

B. It manages the cluster resources directly.

C. It is the main entry point to work with Spark SQL and DataFrames.

D. It replaces the need for any SparkContext.

Solution

1. Step 1: Understand SparkSession's purpose

   SparkSession is designed as the main interface for working with structured data using Spark SQL and DataFrames.

2. Step 2: Differentiate from other components

   SparkContext manages cluster connection but SparkSession provides higher-level APIs for SQL and DataFrames.

3. Final Answer:

   It is the main entry point to work with Spark SQL and DataFrames. -> Option C

4. Quick Check:

   SparkSession = main SQL/DataFrame entry [OK]

Hint: SparkSession is your main tool for SQL and DataFrames [OK]

Common Mistakes:

• Confusing SparkSession with SparkContext's cluster management
• Thinking SparkSession stores data permanently
• Believing SparkSession removes need for SparkContext

2. Which of the following is the correct way to create a SparkSession in PySpark?

easy

A. spark = SparkContext.builder.appName('MyApp').getOrCreate()

B. spark = SparkSession.builder.appName('MyApp').getOrCreate()

C. spark = SparkSession.create('MyApp')

D. spark = SparkContext.create('MyApp')

Solution

1. Step 1: Recall SparkSession creation syntax

   The correct way is to use SparkSession.builder, set app name, then call getOrCreate() to create or get existing session.

2. Step 2: Identify incorrect options

   SparkContext does not have builder or getOrCreate methods; SparkSession.create is not valid syntax.

3. Final Answer:

   spark = SparkSession.builder.appName('MyApp').getOrCreate() -> Option B

4. Quick Check:

   Use SparkSession.builder.appName().getOrCreate() [OK]

Hint: Use SparkSession.builder.appName().getOrCreate() to start [OK]

Common Mistakes:

• Using SparkContext instead of SparkSession to create session
• Calling non-existent create() method
• Missing getOrCreate() at the end

3. Given the following PySpark code, what will be the output of print(sc.appName)?

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestApp').master('local').getOrCreate()
sc = spark.sparkContext
print(sc.appName)

medium

A. None

B. local

C. SparkContext

D. TestApp

Solution

1. Step 1: Understand appName assignment

   The appName 'TestApp' is set when building SparkSession and is passed to SparkContext.

2. Step 2: Access SparkContext appName property

   sc.appName returns the application name set, which is 'TestApp'.

3. Final Answer:

   TestApp -> Option D

4. Quick Check:

   sc.appName = 'TestApp' [OK]

Hint: sc.appName matches SparkSession appName [OK]

Common Mistakes:

• Confusing master URL with appName
• Expecting sc.appName to be 'SparkContext'
• Assuming sc.appName is None by default

4. Identify the error in the following PySpark code snippet:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('App').getOrCreate()
sc = spark.sparkContext
sc = SparkContext('local', 'App')
print(sc.appName)

medium

A. Reassigning sc with SparkContext directly causes error.

B. Missing import for SparkContext.

C. SparkSession.builder.appName() is incorrect syntax.

D. print(sc.appName) is invalid method call.

Solution

1. Step 1: Analyze sc reassignment

   After creating SparkSession and getting sc, the code tries to create a new SparkContext directly, which is not allowed if one already exists.

2. Step 2: Understand SparkContext singleton rule

   Only one SparkContext can exist per JVM; creating another causes an error.

3. Final Answer:

   Reassigning sc with SparkContext directly causes error. -> Option A

4. Quick Check:

   Only one SparkContext allowed; reassigning causes error [OK]

Hint: Do not create SparkContext if SparkSession exists [OK]

Common Mistakes:

• Trying to create multiple SparkContext instances
• Ignoring SparkContext singleton restriction
• Assuming print(sc.appName) is invalid

5. You want to create a SparkSession with the app name 'DataApp' and run it locally using 4 cores. Which code snippet correctly achieves this and also retrieves the SparkContext?

hard

A. spark = SparkSession.builder.appName('DataApp').master('local[4]').getOrCreate()
sc = spark.sparkContext

B. sc = SparkContext('local[4]', 'DataApp')
spark = SparkSession(sc)

C. spark = SparkSession.create('DataApp', master='local[4]')
sc = spark.getContext()

D. spark = SparkSession.builder.master('4').appName('DataApp').getOrCreate()
sc = spark.context

Solution

1. Step 1: Use SparkSession.builder with appName and master

   The correct way is to use builder, set appName to 'DataApp' and master to 'local[4]' for 4 cores, then call getOrCreate().

2. Step 2: Retrieve SparkContext from SparkSession

   Access SparkContext via spark.sparkContext property after creating SparkSession.

3. Final Answer:

   spark = SparkSession.builder.appName('DataApp').master('local[4]').getOrCreate() sc = spark.sparkContext -> Option A

4. Quick Check:

   Use builder.appName().master('local[4]').getOrCreate() + spark.sparkContext [OK]

Hint: Set master='local[4]' and get spark.sparkContext [OK]

Common Mistakes:

• Using SparkContext constructor directly instead of SparkSession.builder
• Wrong master string like '4' instead of 'local[4]'
• Incorrect method names like create() or getContext()